A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

Tianyi Jia; Haiyan Wang; Xiaohong Shen

doi:10.1007/s11235-020-00676-8

A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

Tianyi Jia
, Haiyan Wang
, Xiaohong Shen

航海学院

Northwestern Polytechnical University Xian
Ministry of Industry and Information Technology
Shaanxi University of Science and Technology

科研成果: 期刊稿件 › 快报 › 同行评审

8 引用（Scopus）

摘要

We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

源语言	英语
页（从-至）	531-537
页数	7
期刊	Telecommunication Systems
卷	74
期	4
DOI	https://doi.org/10.1007/s11235-020-00676-8
出版状态	已出版 - 1 8月 2020

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abstract = "We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.",

keywords = "Compressed sensing, DOA estimation, RIPless theory",

author = "Tianyi Jia and Haiyan Wang and Xiaohong Shen",

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AU - Jia, Tianyi

AU - Wang, Haiyan

AU - Shen, Xiaohong

PY - 2020/8/1

Y1 - 2020/8/1

N2 - We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

AB - We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

KW - Compressed sensing

KW - DOA estimation

KW - RIPless theory

UR - https://www.scopus.com/pages/publications/85085999954

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DO - 10.1007/s11235-020-00676-8

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A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

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